Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures

Helder Sebastião, Pedro Godinho and Sjur Westgaard
Authors registered in the RePEc Author Service: Helder Miguel Correia Virtuoso Sebastião

Scientific Annals of Economics and Business (continues Analele Stiintifice), 2020, vol. 67, issue si, 1 - 17

Abstract: This study investigates the use of several trading strategies, based on Machine Learning methods, to profit on the risk premium of the Nordic electricity base-load week futures. The information set is only composed by financial data from January 02, 2006 to November 15, 2017. The results point out that the Support Vector Machine is the best method, but, most importantly, they highlight that all individual models are valuable, in the sense that their combination provides a robust trading procedure, generating an average profit of at least 26% per year, after considering trading costs and liquidity constraints. The results are robust to the different data partitions, and there is no evidence that the profitability of the trading strategies has decreased in recent years. We claim that this market allows for profitable speculation, namely by using combinations of non-linear signal extraction techniques. JEL Codes - G13; G14; Q40

Keywords: Nord Pool; electricity futures; risk premium; machine learning; trading (search for similar items in EconPapers)
Date: 2020
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Journal Article: Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:aic:saebjn:v:67:y:2020:i:si:p:1-17:n:191

DOI: 10.47743/saeb-2020-0024

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